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Jul 6, 2024 · ABSTRACT. In this paper, we propose a new secure machine learning inference platform assisted by a small dedicated security processor, which.
Oct 18, 2022 · In this paper, we propose a new secure machine learning inference platform assisted by a small dedicated security processor, which will be easier to protect ...
Oct 3, 2024 · In this paper, we propose a new secure machine learning inference platform assisted by a small dedicated security processor, which will be ...
Sep 4, 2024 · In this paper, we propose STAMP, an end-to-end 3-party MPC protocol for efficient privacy-preserving machine learning inference assisted by ...
Systems security with more emphasis on hardware-assisted solutions to improve security and privacy. ○ Cross-layer approach: ○ Modeling side-channels.
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S TAMP provides three main advantages over the state-of-the-art MPC protocols, which will be far easier to secure and deploy than today's large TEEs, ...
Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware. No ratings. Presented at Privacy Enhancing Technologies Symposium 2024 by.
In this paper, we propose a hybrid framework integrating SGX and HE, called HT2ML, to protect user's data and models.
Additionally, Penetralium uses a lightweight confidence score perturbation policy to protect against advanced privacy inference attacks on deep learning models.
Dec 8, 2023 · This one day workshop focuses on privacy preserving techniques for training, inference, and disclosure in large scale data analysis.